code: def show_prediction(estimator, doc, **kwargs):
      """Return an explanation of estimator prediction
      as an IPython.display.HTML object. Use this function
      to show information about classifier prediction in IPython.
  
      :func:`show_prediction` accepts all
      :func:`eli5.explain_prediction` arguments and all
      :func:`eli5.formatters.html.format_as_html`
      keyword arguments, so it is possible to get explanation and
      customize formatting in a single call.
  
  
      If :func:`explain_prediction` returns an :class:`base.Explanation` object with
      the ``image`` attribute not set to None, i.e. if explaining image based models,
      then formatting is dispatched to an image display implementation,
      and image explanations are shown in an IPython cell.
      Extra keyword arguments are passed to :func:`eli5.format_as_image`.
  
      Note that this image display implementation
      requires ``matplotlib`` and ``Pillow`` as extra dependencies.
      If the dependencies are missing, no formatting is done
      and the original :class:`base.Explanation` object is returned.
  
  
      Parameters
      ----------
      estimator : object
          Estimator instance. This argument must be positional.
  
      doc : object
          Example to run estimator on. Estimator makes a prediction for this
          example, and :func:`show_prediction` tries to show information
          about this prediction. Pass a single element, not a one-element array:
          if you fitted your estimator on ``X``, that would be ``X[i]`` for
          most containers, and ``X.iloc[i]`` for ``pandas.DataFrame``.
  
      top : int or (int, int) tuple, optional
          Number of features to show. When ``top`` is int, ``top`` features with
          a highest absolute values are shown. When it is (pos, neg) tuple,
          no more than ``pos`` positive features and no more than ``neg``
          negative features is shown. ``None`` value means no limit (default).
  
          This argument may be supported or not, depending on estimator type.
  
      top_targets : int, optional
          Number of targets to show. When ``top_targets`` is provided,
          only specified number of targets with highest scores are shown.
          Negative value means targets with lowest scores are shown.
          Must not be given with ``targets`` argument.
          ``None`` value means no limit: all targets are shown (default).
  
          This argument may be supported or not, depending on estimator type.
  
      target_names : list[str] or {'old_name': 'new_name'} dict, optional
          Names of targets or classes. This argument can be used to provide
          human-readable class/target names for estimators which don't expose
          clss names themselves. It can be also used to rename estimator-provided
          classes before displaying them.
  
          This argument may be supported or not, depending on estimator type.
  
      targets : list, optional
          Order of class/target names to show. This argument can be also used
          to show information only for a subset of classes. It should be a list
          of class / target names which match either names provided by
          an estimator or names defined in ``target_names`` parameter.
  
          In case of binary classification you can use this argument to
          set the class which probability or score should be displayed, with
          an appropriate explanation. By default a result for predicted class
          is shown. For example, you can use ``targets=[True]`` to always show
          result for a positive class, even if the predicted label is False.
  
          This argument may be supported or not, depending on estimator type.
  
      feature_names : list, optional
          A list of feature names. It allows to specify feature
          names when they are not provided by an estimator object.
  
          This argument may be supported or not, depending on estimator type.
  
      feature_re : str, optional
          Only feature names which match ``feature_re`` regex are shown
          (more precisely, ``re.search(feature_re, x)`` is checked).
  
      feature_filter : Callable[[str, float], bool], optional
          Only feature names for which ``feature_filter`` function returns True
          are shown. It must accept feature name and feature value.
          Missing features always have a NaN value.
  
      show : List[str], optional
          List of sections to show. Allowed values:
  
          * 'targets' - per-target feature weights;
          * 'transition_features' - transition features of a CRF model;
          * 'feature_importances' - feature importances of a decision tree or
            an ensemble-based estimator;
          * 'decision_tree' - decision tree in a graphical form;
          * 'method' - a string with explanation method;
          * 'description' - description of explanation method and its caveats.
  
          ``eli5.formatters.fields`` provides constants that cover common cases:
          ``INFO`` (method and description), ``WEIGHTS`` (all the rest),
          and ``ALL`` (all).
  
      horizontal_layout : bool
          When True, feature weight tables are printed horizontally
          (left to right); when False, feature weight tables are printed
          vertically (top to down). Default is True.
  
      highlight_spaces : bool or None, optional
          Whether to highlight spaces in feature names. This is useful if
          you work with text and have ngram features which may include spaces
          at left or right. Default is None, meaning that the value used
          is set automatically based on vectorizer and feature values.
  
      include_styles : bool
          Most styles are inline, but some are included separately in <style> tag;
          you can omit them by passing ``include_styles=False``. Default is True.
  
      force_weights : bool
          When True, a table with feature weights is displayed even if all
          features are already highlighted in text. Default is False.
  
      preserve_density: bool or None
          This argument currently only makes sense when used with text data
          and vectorizers from scikit-learn.
  
          If preserve_density is True, then color for longer fragments will be
          less intensive than for shorter fragments, so that "sum" of intensities
          will correspond to feature weight.
  
          If preserve_density is None, then it's value is chosen depending on
          analyzer kind: it is preserved for "char" and "char_wb" analyzers,
          and not preserved for "word" analyzers.
  
          Default is None.
  
      show_feature_values : bool
          When True, feature values are shown along with feature contributions.
          Default is False.
  
      **kwargs: dict
          Keyword arguments. All keyword arguments are passed to
          concrete explain_prediction... implementations.
  
      Returns
      -------
      IPython.display.HTML
          The result is printed in IPython notebook as an HTML widget.
          If you need to display several explanations as an output of a single
          cell, or if you want to display it from a function then use
          IPython.display.display::
  
              from IPython.display import display
              display(eli5.show_weights(clf1))
              display(eli5.show_weights(clf2))
  
      PIL.Image.Image
          Image with a heatmap overlay, *if explaining image based models*.
          The image is shown in an IPython notebook cell
          if it is the last thing returned.
          To display the image in a loop, function, or other case,
          use IPython.display.display::
  
              from IPython.display import display
              for cls_idx in [0, 432]:
                  display(eli5.show_prediction(clf, doc, targets=[cls_idx]))
  
      """
      format_kwargs, explain_kwargs = _split_kwargs(kwargs)
      expl = explain_prediction(estimator, doc, **explain_kwargs)
      if expl.image is not None:
          # dispatch to image display implementation
          if isinstance(format_as_image, ImportError):
              warnings.warn(
                  'Missing dependencies: "{}". '
                  "Returning original Explanation.".format(
                      format_as_image
                  )
              )
              return expl
          else:
              return format_as_image(expl, **format_kwargs)
      else:
          # use default implementation
          # TODO: a better design / refactorings might be needed
          _set_html_kwargs_defaults(format_kwargs)
          html = format_as_html(expl, **format_kwargs)
          return HTML(html)
  
file_path: eli5/eli5/ipython.py
imports: from __future__ import absolute_import
  from typing import Any, Dict, Tuple
  import warnings
  from IPython.display import HTML, Image
  from .explain import explain_weights, explain_prediction
  from .formatters import format_as_html, fields
label_desc: Use image node_id 2 for calling a global function with example usage: show_prediction(estimator, doc) and returns: HTML, expl, format_as_image